Valueerror: Gefunden Eingangsvariablen mit inkonsistenter Anzahl von Proben: [4, 3255], Python3

Question

Beim Versuch, meinen Code ich folgende Fehlermeldung

Valueerror erhalten auszuführen: Gefunden Eingangsvariablen mit inkonsistenter Anzahl von Proben: [4, 3255], Python3

Ich verstehe, dass es eine Art Formatfehler ist, aber ich weiß nicht, wie man es löst.

import pandas as pd 
import quandl, math 
import numpy as np 
from sklearn import preprocessing, svm, cross_validation 
from sklearn.linear_model import LinearRegression 

df = quandl.get('WIKI/GOOGL') 
df = df [['Adj. Open','Adj. High','Adj. Low','Adj. Close','Adj. Volume',]] 
df['HL_PCT'] = (df['Adj. High'] - df['Adj. Open'])/df['Adj. Open'] * 100 
df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open'])/df['Adj. Open'] * 100 

df = df[['Adj. Close','HL_PCT','PCT_change','Adj. Volume']] 

forecast_col = 'Adj. Close' 
df.fillna(-99999, inplace=True) 

forecast_out = int(math.ceil(0.01*len(df))) 

df['label'] = df[forecast_col].shift(-forecast_out) 


x = np.array(df.drop(['label'],1)) 
x = preprocessing.scale(x) 
x = x[-forecast_out] 
x_lately = x[-forecast_out:] 


df.dropna(inplace=True) 
y = np.array(df['label']) 
y = np.array(df['label']) 



x_train, x_test, y_train, y_test = cross_validation.train_test_split(x, y, test_size=0.2) 

clf = LinearRegression() 
clf.fit(x_train, y_train) 

accuracy = clf.score(x_test, y_test) 

print(accuracy) 

Traceback:

Traceback (most recent call last): 
    File "main.py", line 33, in <module> 
    x_train, x_test, y_train, y_test = cross_validation.train_test_split(x, y, test_size=0.2) 
    File "C:\Users\User\Python\lib\site-packages\sklearn\cross_validation.py", line 2059, in train_test_split 
    arrays = indexable(*arrays) 
    File "C:\Users\User\Python\lib\site-packages\sklearn\utils\validation.py", line 198, in indexable 
    check_consistent_length(*result) 
    File "C:\Users\User\Python\lib\site-packages\sklearn\utils\validation.py", line 173, in check_consistent_length 
    " samples: %r" % [int(l) for l in lengths]) 
ValueError: Found input variables with inconsistent numbers of samples: [4, 3255] 

Answer 1

fand ich das Problem. Dies ist, wie sollte der Code formatiert werden, damit es funktioniert:

X = np.array (df.drop ([ 'label'], 1))

X = preprocessing.scale (X)

X_lately = X [-forecast_out:]

X = X [: - forecast_out:]